计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 219-223.doi: 10.11896/jsjkx.200100087
马传香1,2, 汪炀杰1, 王旭1
MA Chuan-xiang1,2, WANG Yang-jie1, WANG Xu1
摘要： 为了杜绝或避免矿产品资源如煤炭、砂石矿等行业因不开票而导致偷税漏税现象的发生,利用深度卷积神经网络自动识别空车重车是一种有效途径。本文在AlexNet模型基础上,针对空车重车图像的差异性,提出5种改进思路,最终得到一种基于maxout+dropout的6层卷积神经网络的结构。对34 220张空车重车图片的测试结果表明,模型在准确度、敏感度、特异性、精度等方面都取得了良好的效果。此外,模型还具有高度的鲁棒性,可以成功识别大量不同角度和不同场景的空车重车图像。
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